| Current File : //usr/local/lib64/python3.6/site-packages/pandas/core/arrays/interval.py |
from operator import le, lt
import textwrap
import numpy as np
from pandas._config import get_option
from pandas._libs.interval import Interval, IntervalMixin, intervals_to_interval_bounds
from pandas.compat.numpy import function as nv
from pandas.util._decorators import Appender
from pandas.core.dtypes.cast import maybe_convert_platform
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_datetime64_any_dtype,
is_float_dtype,
is_integer_dtype,
is_interval,
is_interval_dtype,
is_list_like,
is_object_dtype,
is_scalar,
is_string_dtype,
is_timedelta64_dtype,
pandas_dtype,
)
from pandas.core.dtypes.dtypes import IntervalDtype
from pandas.core.dtypes.generic import (
ABCDatetimeIndex,
ABCIndexClass,
ABCIntervalIndex,
ABCPeriodIndex,
ABCSeries,
)
from pandas.core.dtypes.missing import isna, notna
from pandas.core.algorithms import take, value_counts
from pandas.core.arrays.base import ExtensionArray, _extension_array_shared_docs
from pandas.core.arrays.categorical import Categorical
import pandas.core.common as com
from pandas.core.construction import array
from pandas.core.indexers import check_array_indexer
from pandas.core.indexes.base import ensure_index
_VALID_CLOSED = {"left", "right", "both", "neither"}
_interval_shared_docs = {}
_shared_docs_kwargs = dict(
klass="IntervalArray", qualname="arrays.IntervalArray", name=""
)
_interval_shared_docs[
"class"
] = """
%(summary)s
.. versionadded:: %(versionadded)s
Parameters
----------
data : array-like (1-dimensional)
Array-like containing Interval objects from which to build the
%(klass)s.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both or
neither.
dtype : dtype or None, default None
If None, dtype will be inferred.
.. versionadded:: 0.23.0
copy : bool, default False
Copy the input data.
%(name)s\
verify_integrity : bool, default True
Verify that the %(klass)s is valid.
Attributes
----------
left
right
closed
mid
length
is_empty
is_non_overlapping_monotonic
%(extra_attributes)s\
Methods
-------
from_arrays
from_tuples
from_breaks
contains
overlaps
set_closed
to_tuples
%(extra_methods)s\
See Also
--------
Index : The base pandas Index type.
Interval : A bounded slice-like interval; the elements of an %(klass)s.
interval_range : Function to create a fixed frequency IntervalIndex.
cut : Bin values into discrete Intervals.
qcut : Bin values into equal-sized Intervals based on rank or sample quantiles.
Notes
-----
See the `user guide
<https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#intervalindex>`_
for more.
%(examples)s\
"""
@Appender(
_interval_shared_docs["class"]
% dict(
klass="IntervalArray",
summary="Pandas array for interval data that are closed on the same side.",
versionadded="0.24.0",
name="",
extra_attributes="",
extra_methods="",
examples=textwrap.dedent(
"""\
Examples
--------
A new ``IntervalArray`` can be constructed directly from an array-like of
``Interval`` objects:
>>> pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)])
<IntervalArray>
[(0, 1], (1, 5]]
Length: 2, closed: right, dtype: interval[int64]
It may also be constructed using one of the constructor
methods: :meth:`IntervalArray.from_arrays`,
:meth:`IntervalArray.from_breaks`, and :meth:`IntervalArray.from_tuples`.
"""
),
)
)
class IntervalArray(IntervalMixin, ExtensionArray):
ndim = 1
can_hold_na = True
_na_value = _fill_value = np.nan
def __new__(
cls,
data,
closed=None,
dtype=None,
copy: bool = False,
verify_integrity: bool = True,
):
if isinstance(data, ABCSeries) and is_interval_dtype(data.dtype):
data = data._values
if isinstance(data, (cls, ABCIntervalIndex)):
left = data.left
right = data.right
closed = closed or data.closed
else:
# don't allow scalars
if is_scalar(data):
msg = (
f"{cls.__name__}(...) must be called with a collection "
f"of some kind, {data} was passed"
)
raise TypeError(msg)
# might need to convert empty or purely na data
data = maybe_convert_platform_interval(data)
left, right, infer_closed = intervals_to_interval_bounds(
data, validate_closed=closed is None
)
closed = closed or infer_closed
return cls._simple_new(
left,
right,
closed,
copy=copy,
dtype=dtype,
verify_integrity=verify_integrity,
)
@classmethod
def _simple_new(
cls, left, right, closed=None, copy=False, dtype=None, verify_integrity=True
):
result = IntervalMixin.__new__(cls)
closed = closed or "right"
left = ensure_index(left, copy=copy)
right = ensure_index(right, copy=copy)
if dtype is not None:
# GH 19262: dtype must be an IntervalDtype to override inferred
dtype = pandas_dtype(dtype)
if not is_interval_dtype(dtype):
msg = f"dtype must be an IntervalDtype, got {dtype}"
raise TypeError(msg)
elif dtype.subtype is not None:
left = left.astype(dtype.subtype)
right = right.astype(dtype.subtype)
# coerce dtypes to match if needed
if is_float_dtype(left) and is_integer_dtype(right):
right = right.astype(left.dtype)
elif is_float_dtype(right) and is_integer_dtype(left):
left = left.astype(right.dtype)
if type(left) != type(right):
msg = (
f"must not have differing left [{type(left).__name__}] and "
f"right [{type(right).__name__}] types"
)
raise ValueError(msg)
elif is_categorical_dtype(left.dtype) or is_string_dtype(left.dtype):
# GH 19016
msg = (
"category, object, and string subtypes are not supported "
"for IntervalArray"
)
raise TypeError(msg)
elif isinstance(left, ABCPeriodIndex):
msg = "Period dtypes are not supported, use a PeriodIndex instead"
raise ValueError(msg)
elif isinstance(left, ABCDatetimeIndex) and str(left.tz) != str(right.tz):
msg = (
"left and right must have the same time zone, got "
f"'{left.tz}' and '{right.tz}'"
)
raise ValueError(msg)
result._left = left
result._right = right
result._closed = closed
if verify_integrity:
result._validate()
return result
@classmethod
def _from_sequence(cls, scalars, dtype=None, copy=False):
return cls(scalars, dtype=dtype, copy=copy)
@classmethod
def _from_factorized(cls, values, original):
if len(values) == 0:
# An empty array returns object-dtype here. We can't create
# a new IA from an (empty) object-dtype array, so turn it into the
# correct dtype.
values = values.astype(original.dtype.subtype)
return cls(values, closed=original.closed)
_interval_shared_docs["from_breaks"] = textwrap.dedent(
"""
Construct an %(klass)s from an array of splits.
Parameters
----------
breaks : array-like (1-dimensional)
Left and right bounds for each interval.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both
or neither.
copy : bool, default False
Copy the data.
dtype : dtype or None, default None
If None, dtype will be inferred.
.. versionadded:: 0.23.0
Returns
-------
%(klass)s
See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
%(klass)s.from_arrays : Construct from a left and right array.
%(klass)s.from_tuples : Construct from a sequence of tuples.
%(examples)s\
"""
)
@classmethod
@Appender(
_interval_shared_docs["from_breaks"]
% dict(
klass="IntervalArray",
examples=textwrap.dedent(
"""\
Examples
--------
>>> pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3])
<IntervalArray>
[(0, 1], (1, 2], (2, 3]]
Length: 3, closed: right, dtype: interval[int64]
"""
),
)
)
def from_breaks(cls, breaks, closed="right", copy=False, dtype=None):
breaks = maybe_convert_platform_interval(breaks)
return cls.from_arrays(breaks[:-1], breaks[1:], closed, copy=copy, dtype=dtype)
_interval_shared_docs["from_arrays"] = textwrap.dedent(
"""
Construct from two arrays defining the left and right bounds.
Parameters
----------
left : array-like (1-dimensional)
Left bounds for each interval.
right : array-like (1-dimensional)
Right bounds for each interval.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both
or neither.
copy : bool, default False
Copy the data.
dtype : dtype, optional
If None, dtype will be inferred.
.. versionadded:: 0.23.0
Returns
-------
%(klass)s
Raises
------
ValueError
When a value is missing in only one of `left` or `right`.
When a value in `left` is greater than the corresponding value
in `right`.
See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
%(klass)s.from_breaks : Construct an %(klass)s from an array of
splits.
%(klass)s.from_tuples : Construct an %(klass)s from an
array-like of tuples.
Notes
-----
Each element of `left` must be less than or equal to the `right`
element at the same position. If an element is missing, it must be
missing in both `left` and `right`. A TypeError is raised when
using an unsupported type for `left` or `right`. At the moment,
'category', 'object', and 'string' subtypes are not supported.
%(examples)s\
"""
)
@classmethod
@Appender(
_interval_shared_docs["from_arrays"]
% dict(
klass="IntervalArray",
examples=textwrap.dedent(
"""\
>>> pd.arrays.IntervalArray.from_arrays([0, 1, 2], [1, 2, 3])
<IntervalArray>
[(0, 1], (1, 2], (2, 3]]
Length: 3, closed: right, dtype: interval[int64]
"""
),
)
)
def from_arrays(cls, left, right, closed="right", copy=False, dtype=None):
left = maybe_convert_platform_interval(left)
right = maybe_convert_platform_interval(right)
return cls._simple_new(
left, right, closed, copy=copy, dtype=dtype, verify_integrity=True
)
_interval_shared_docs["from_tuples"] = textwrap.dedent(
"""
Construct an %(klass)s from an array-like of tuples.
Parameters
----------
data : array-like (1-dimensional)
Array of tuples.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both
or neither.
copy : bool, default False
By-default copy the data, this is compat only and ignored.
dtype : dtype or None, default None
If None, dtype will be inferred.
.. versionadded:: 0.23.0
Returns
-------
%(klass)s
See Also
--------
interval_range : Function to create a fixed frequency IntervalIndex.
%(klass)s.from_arrays : Construct an %(klass)s from a left and
right array.
%(klass)s.from_breaks : Construct an %(klass)s from an array of
splits.
%(examples)s\
"""
)
@classmethod
@Appender(
_interval_shared_docs["from_tuples"]
% dict(
klass="IntervalArray",
examples=textwrap.dedent(
"""\
Examples
--------
>>> pd.arrays.IntervalArray.from_tuples([(0, 1), (1, 2)])
<IntervalArray>
[(0, 1], (1, 2]]
Length: 2, closed: right, dtype: interval[int64]
"""
),
)
)
def from_tuples(cls, data, closed="right", copy=False, dtype=None):
if len(data):
left, right = [], []
else:
# ensure that empty data keeps input dtype
left = right = data
for d in data:
if isna(d):
lhs = rhs = np.nan
else:
name = cls.__name__
try:
# need list of length 2 tuples, e.g. [(0, 1), (1, 2), ...]
lhs, rhs = d
except ValueError as err:
msg = f"{name}.from_tuples requires tuples of length 2, got {d}"
raise ValueError(msg) from err
except TypeError as err:
msg = f"{name}.from_tuples received an invalid item, {d}"
raise TypeError(msg) from err
left.append(lhs)
right.append(rhs)
return cls.from_arrays(left, right, closed, copy=False, dtype=dtype)
def _validate(self):
"""
Verify that the IntervalArray is valid.
Checks that
* closed is valid
* left and right match lengths
* left and right have the same missing values
* left is always below right
"""
if self.closed not in _VALID_CLOSED:
msg = f"invalid option for 'closed': {self.closed}"
raise ValueError(msg)
if len(self.left) != len(self.right):
msg = "left and right must have the same length"
raise ValueError(msg)
left_mask = notna(self.left)
right_mask = notna(self.right)
if not (left_mask == right_mask).all():
msg = (
"missing values must be missing in the same "
"location both left and right sides"
)
raise ValueError(msg)
if not (self.left[left_mask] <= self.right[left_mask]).all():
msg = "left side of interval must be <= right side"
raise ValueError(msg)
# ---------
# Interface
# ---------
def __iter__(self):
return iter(np.asarray(self))
def __len__(self) -> int:
return len(self.left)
def __getitem__(self, value):
value = check_array_indexer(self, value)
left = self.left[value]
right = self.right[value]
# scalar
if not isinstance(left, ABCIndexClass):
if is_scalar(left) and isna(left):
return self._fill_value
if np.ndim(left) > 1:
# GH#30588 multi-dimensional indexer disallowed
raise ValueError("multi-dimensional indexing not allowed")
return Interval(left, right, self.closed)
return self._shallow_copy(left, right)
def __setitem__(self, key, value):
# na value: need special casing to set directly on numpy arrays
needs_float_conversion = False
if is_scalar(value) and isna(value):
if is_integer_dtype(self.dtype.subtype):
# can't set NaN on a numpy integer array
needs_float_conversion = True
elif is_datetime64_any_dtype(self.dtype.subtype):
# need proper NaT to set directly on the numpy array
value = np.datetime64("NaT")
elif is_timedelta64_dtype(self.dtype.subtype):
# need proper NaT to set directly on the numpy array
value = np.timedelta64("NaT")
value_left, value_right = value, value
# scalar interval
elif is_interval_dtype(value) or isinstance(value, Interval):
self._check_closed_matches(value, name="value")
value_left, value_right = value.left, value.right
else:
# list-like of intervals
try:
array = IntervalArray(value)
value_left, value_right = array.left, array.right
except TypeError as err:
# wrong type: not interval or NA
msg = f"'value' should be an interval type, got {type(value)} instead."
raise TypeError(msg) from err
if needs_float_conversion:
raise ValueError("Cannot set float NaN to integer-backed IntervalArray")
key = check_array_indexer(self, key)
# Need to ensure that left and right are updated atomically, so we're
# forced to copy, update the copy, and swap in the new values.
left = self.left.copy(deep=True)
left._values[key] = value_left
self._left = left
right = self.right.copy(deep=True)
right._values[key] = value_right
self._right = right
def __eq__(self, other):
# ensure pandas array for list-like and eliminate non-interval scalars
if is_list_like(other):
if len(self) != len(other):
raise ValueError("Lengths must match to compare")
other = array(other)
elif not isinstance(other, Interval):
# non-interval scalar -> no matches
return np.zeros(len(self), dtype=bool)
# determine the dtype of the elements we want to compare
if isinstance(other, Interval):
other_dtype = pandas_dtype("interval")
elif not is_categorical_dtype(other.dtype):
other_dtype = other.dtype
else:
# for categorical defer to categories for dtype
other_dtype = other.categories.dtype
# extract intervals if we have interval categories with matching closed
if is_interval_dtype(other_dtype):
if self.closed != other.categories.closed:
return np.zeros(len(self), dtype=bool)
other = other.categories.take(other.codes)
# interval-like -> need same closed and matching endpoints
if is_interval_dtype(other_dtype):
if self.closed != other.closed:
return np.zeros(len(self), dtype=bool)
return (self.left == other.left) & (self.right == other.right)
# non-interval/non-object dtype -> no matches
if not is_object_dtype(other_dtype):
return np.zeros(len(self), dtype=bool)
# object dtype -> iteratively check for intervals
result = np.zeros(len(self), dtype=bool)
for i, obj in enumerate(other):
# need object to be an Interval with same closed and endpoints
if (
isinstance(obj, Interval)
and self.closed == obj.closed
and self.left[i] == obj.left
and self.right[i] == obj.right
):
result[i] = True
return result
def fillna(self, value=None, method=None, limit=None):
"""
Fill NA/NaN values using the specified method.
Parameters
----------
value : scalar, dict, Series
If a scalar value is passed it is used to fill all missing values.
Alternatively, a Series or dict can be used to fill in different
values for each index. The value should not be a list. The
value(s) passed should be either Interval objects or NA/NaN.
method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
(Not implemented yet for IntervalArray)
Method to use for filling holes in reindexed Series
limit : int, default None
(Not implemented yet for IntervalArray)
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled.
Returns
-------
filled : IntervalArray with NA/NaN filled
"""
if method is not None:
raise TypeError("Filling by method is not supported for IntervalArray.")
if limit is not None:
raise TypeError("limit is not supported for IntervalArray.")
if not isinstance(value, Interval):
msg = (
"'IntervalArray.fillna' only supports filling with a "
f"scalar 'pandas.Interval'. Got a '{type(value).__name__}' instead."
)
raise TypeError(msg)
self._check_closed_matches(value, name="value")
left = self.left.fillna(value=value.left)
right = self.right.fillna(value=value.right)
return self._shallow_copy(left, right)
@property
def dtype(self):
return IntervalDtype(self.left.dtype)
def astype(self, dtype, copy=True):
"""
Cast to an ExtensionArray or NumPy array with dtype 'dtype'.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
copy : bool, default True
Whether to copy the data, even if not necessary. If False,
a copy is made only if the old dtype does not match the
new dtype.
Returns
-------
array : ExtensionArray or ndarray
ExtensionArray or NumPy ndarray with 'dtype' for its dtype.
"""
from pandas.core.arrays.string_ import StringDtype
if dtype is not None:
dtype = pandas_dtype(dtype)
if is_interval_dtype(dtype):
if dtype == self.dtype:
return self.copy() if copy else self
# need to cast to different subtype
try:
new_left = self.left.astype(dtype.subtype)
new_right = self.right.astype(dtype.subtype)
except TypeError as err:
msg = (
f"Cannot convert {self.dtype} to {dtype}; subtypes are incompatible"
)
raise TypeError(msg) from err
return self._shallow_copy(new_left, new_right)
elif is_categorical_dtype(dtype):
return Categorical(np.asarray(self))
elif isinstance(dtype, StringDtype):
return dtype.construct_array_type()._from_sequence(self, copy=False)
# TODO: This try/except will be repeated.
try:
return np.asarray(self).astype(dtype, copy=copy)
except (TypeError, ValueError) as err:
msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
raise TypeError(msg) from err
@classmethod
def _concat_same_type(cls, to_concat):
"""
Concatenate multiple IntervalArray
Parameters
----------
to_concat : sequence of IntervalArray
Returns
-------
IntervalArray
"""
closed = {interval.closed for interval in to_concat}
if len(closed) != 1:
raise ValueError("Intervals must all be closed on the same side.")
closed = closed.pop()
left = np.concatenate([interval.left for interval in to_concat])
right = np.concatenate([interval.right for interval in to_concat])
return cls._simple_new(left, right, closed=closed, copy=False)
def _shallow_copy(self, left, right):
"""
Return a new IntervalArray with the replacement attributes
Parameters
----------
left : Index
Values to be used for the left-side of the intervals.
right : Index
Values to be used for the right-side of the intervals.
"""
return self._simple_new(left, right, closed=self.closed, verify_integrity=False)
def copy(self):
"""
Return a copy of the array.
Returns
-------
IntervalArray
"""
left = self.left.copy(deep=True)
right = self.right.copy(deep=True)
closed = self.closed
# TODO: Could skip verify_integrity here.
return type(self).from_arrays(left, right, closed=closed)
def isna(self):
return isna(self.left)
@property
def nbytes(self) -> int:
return self.left.nbytes + self.right.nbytes
@property
def size(self) -> int:
# Avoid materializing self.values
return self.left.size
def shift(self, periods: int = 1, fill_value: object = None) -> "IntervalArray":
if not len(self) or periods == 0:
return self.copy()
if isna(fill_value):
fill_value = self.dtype.na_value
# ExtensionArray.shift doesn't work for two reasons
# 1. IntervalArray.dtype.na_value may not be correct for the dtype.
# 2. IntervalArray._from_sequence only accepts NaN for missing values,
# not other values like NaT
empty_len = min(abs(periods), len(self))
if isna(fill_value):
fill_value = self.left._na_value
empty = IntervalArray.from_breaks([fill_value] * (empty_len + 1))
else:
empty = self._from_sequence([fill_value] * empty_len)
if periods > 0:
a = empty
b = self[:-periods]
else:
a = self[abs(periods) :]
b = empty
return self._concat_same_type([a, b])
def take(self, indices, allow_fill=False, fill_value=None, axis=None, **kwargs):
"""
Take elements from the IntervalArray.
Parameters
----------
indices : sequence of integers
Indices to be taken.
allow_fill : bool, default False
How to handle negative values in `indices`.
* False: negative values in `indices` indicate positional indices
from the right (the default). This is similar to
:func:`numpy.take`.
* True: negative values in `indices` indicate
missing values. These values are set to `fill_value`. Any other
other negative values raise a ``ValueError``.
fill_value : Interval or NA, optional
Fill value to use for NA-indices when `allow_fill` is True.
This may be ``None``, in which case the default NA value for
the type, ``self.dtype.na_value``, is used.
For many ExtensionArrays, there will be two representations of
`fill_value`: a user-facing "boxed" scalar, and a low-level
physical NA value. `fill_value` should be the user-facing version,
and the implementation should handle translating that to the
physical version for processing the take if necessary.
axis : any, default None
Present for compat with IntervalIndex; does nothing.
Returns
-------
IntervalArray
Raises
------
IndexError
When the indices are out of bounds for the array.
ValueError
When `indices` contains negative values other than ``-1``
and `allow_fill` is True.
"""
nv.validate_take(tuple(), kwargs)
fill_left = fill_right = fill_value
if allow_fill:
if fill_value is None:
fill_left = fill_right = self.left._na_value
elif is_interval(fill_value):
self._check_closed_matches(fill_value, name="fill_value")
fill_left, fill_right = fill_value.left, fill_value.right
elif not is_scalar(fill_value) and notna(fill_value):
msg = (
"'IntervalArray.fillna' only supports filling with a "
"'scalar pandas.Interval or NA'. "
f"Got a '{type(fill_value).__name__}' instead."
)
raise ValueError(msg)
left_take = take(
self.left, indices, allow_fill=allow_fill, fill_value=fill_left
)
right_take = take(
self.right, indices, allow_fill=allow_fill, fill_value=fill_right
)
return self._shallow_copy(left_take, right_take)
def value_counts(self, dropna=True):
"""
Returns a Series containing counts of each interval.
Parameters
----------
dropna : bool, default True
Don't include counts of NaN.
Returns
-------
counts : Series
See Also
--------
Series.value_counts
"""
# TODO: implement this is a non-naive way!
return value_counts(np.asarray(self), dropna=dropna)
# Formatting
def _format_data(self):
# TODO: integrate with categorical and make generic
# name argument is unused here; just for compat with base / categorical
n = len(self)
max_seq_items = min((get_option("display.max_seq_items") or n) // 10, 10)
formatter = str
if n == 0:
summary = "[]"
elif n == 1:
first = formatter(self[0])
summary = f"[{first}]"
elif n == 2:
first = formatter(self[0])
last = formatter(self[-1])
summary = f"[{first}, {last}]"
else:
if n > max_seq_items:
n = min(max_seq_items // 2, 10)
head = [formatter(x) for x in self[:n]]
tail = [formatter(x) for x in self[-n:]]
head_str = ", ".join(head)
tail_str = ", ".join(tail)
summary = f"[{head_str} ... {tail_str}]"
else:
tail = [formatter(x) for x in self]
tail_str = ", ".join(tail)
summary = f"[{tail_str}]"
return summary
def __repr__(self) -> str:
# the short repr has no trailing newline, while the truncated
# repr does. So we include a newline in our template, and strip
# any trailing newlines from format_object_summary
data = self._format_data()
class_name = f"<{type(self).__name__}>\n"
template = (
f"{class_name}"
f"{data}\n"
f"Length: {len(self)}, closed: {self.closed}, dtype: {self.dtype}"
)
return template
def _format_space(self):
space = " " * (len(type(self).__name__) + 1)
return f"\n{space}"
@property
def left(self):
"""
Return the left endpoints of each Interval in the IntervalArray as
an Index.
"""
return self._left
@property
def right(self):
"""
Return the right endpoints of each Interval in the IntervalArray as
an Index.
"""
return self._right
@property
def closed(self):
"""
Whether the intervals are closed on the left-side, right-side, both or
neither.
"""
return self._closed
_interval_shared_docs["set_closed"] = textwrap.dedent(
"""
Return an %(klass)s identical to the current one, but closed on the
specified side.
.. versionadded:: 0.24.0
Parameters
----------
closed : {'left', 'right', 'both', 'neither'}
Whether the intervals are closed on the left-side, right-side, both
or neither.
Returns
-------
new_index : %(klass)s
%(examples)s\
"""
)
@Appender(
_interval_shared_docs["set_closed"]
% dict(
klass="IntervalArray",
examples=textwrap.dedent(
"""\
Examples
--------
>>> index = pd.arrays.IntervalArray.from_breaks(range(4))
>>> index
<IntervalArray>
[(0, 1], (1, 2], (2, 3]]
Length: 3, closed: right, dtype: interval[int64]
>>> index.set_closed('both')
<IntervalArray>
[[0, 1], [1, 2], [2, 3]]
Length: 3, closed: both, dtype: interval[int64]
"""
),
)
)
def set_closed(self, closed):
if closed not in _VALID_CLOSED:
msg = f"invalid option for 'closed': {closed}"
raise ValueError(msg)
return type(self)._simple_new(
left=self.left, right=self.right, closed=closed, verify_integrity=False
)
@property
def length(self):
"""
Return an Index with entries denoting the length of each Interval in
the IntervalArray.
"""
try:
return self.right - self.left
except TypeError as err:
# length not defined for some types, e.g. string
msg = (
"IntervalArray contains Intervals without defined length, "
"e.g. Intervals with string endpoints"
)
raise TypeError(msg) from err
@property
def mid(self):
"""
Return the midpoint of each Interval in the IntervalArray as an Index.
"""
try:
return 0.5 * (self.left + self.right)
except TypeError:
# datetime safe version
return self.left + 0.5 * self.length
_interval_shared_docs[
"is_non_overlapping_monotonic"
] = """
Return True if the %(klass)s is non-overlapping (no Intervals share
points) and is either monotonic increasing or monotonic decreasing,
else False.
"""
# https://github.com/python/mypy/issues/1362
# Mypy does not support decorated properties
@property # type: ignore
@Appender(
_interval_shared_docs["is_non_overlapping_monotonic"] % _shared_docs_kwargs
)
def is_non_overlapping_monotonic(self):
# must be increasing (e.g., [0, 1), [1, 2), [2, 3), ... )
# or decreasing (e.g., [-1, 0), [-2, -1), [-3, -2), ...)
# we already require left <= right
# strict inequality for closed == 'both'; equality implies overlapping
# at a point when both sides of intervals are included
if self.closed == "both":
return bool(
(self.right[:-1] < self.left[1:]).all()
or (self.left[:-1] > self.right[1:]).all()
)
# non-strict inequality when closed != 'both'; at least one side is
# not included in the intervals, so equality does not imply overlapping
return bool(
(self.right[:-1] <= self.left[1:]).all()
or (self.left[:-1] >= self.right[1:]).all()
)
# Conversion
def __array__(self, dtype=None) -> np.ndarray:
"""
Return the IntervalArray's data as a numpy array of Interval
objects (with dtype='object')
"""
left = self.left
right = self.right
mask = self.isna()
closed = self._closed
result = np.empty(len(left), dtype=object)
for i in range(len(left)):
if mask[i]:
result[i] = np.nan
else:
result[i] = Interval(left[i], right[i], closed)
return result
def __arrow_array__(self, type=None):
"""
Convert myself into a pyarrow Array.
"""
import pyarrow
from pandas.core.arrays._arrow_utils import ArrowIntervalType
try:
subtype = pyarrow.from_numpy_dtype(self.dtype.subtype)
except TypeError as err:
raise TypeError(
f"Conversion to arrow with subtype '{self.dtype.subtype}' "
"is not supported"
) from err
interval_type = ArrowIntervalType(subtype, self.closed)
storage_array = pyarrow.StructArray.from_arrays(
[
pyarrow.array(self.left, type=subtype, from_pandas=True),
pyarrow.array(self.right, type=subtype, from_pandas=True),
],
names=["left", "right"],
)
mask = self.isna()
if mask.any():
# if there are missing values, set validity bitmap also on the array level
null_bitmap = pyarrow.array(~mask).buffers()[1]
storage_array = pyarrow.StructArray.from_buffers(
storage_array.type,
len(storage_array),
[null_bitmap],
children=[storage_array.field(0), storage_array.field(1)],
)
if type is not None:
if type.equals(interval_type.storage_type):
return storage_array
elif isinstance(type, ArrowIntervalType):
# ensure we have the same subtype and closed attributes
if not type.equals(interval_type):
raise TypeError(
"Not supported to convert IntervalArray to type with "
f"different 'subtype' ({self.dtype.subtype} vs {type.subtype}) "
f"and 'closed' ({self.closed} vs {type.closed}) attributes"
)
else:
raise TypeError(
f"Not supported to convert IntervalArray to '{type}' type"
)
return pyarrow.ExtensionArray.from_storage(interval_type, storage_array)
_interval_shared_docs[
"to_tuples"
] = """
Return an %(return_type)s of tuples of the form (left, right).
Parameters
----------
na_tuple : bool, default True
Returns NA as a tuple if True, ``(nan, nan)``, or just as the NA
value itself if False, ``nan``.
.. versionadded:: 0.23.0
Returns
-------
tuples: %(return_type)s
%(examples)s\
"""
@Appender(
_interval_shared_docs["to_tuples"] % dict(return_type="ndarray", examples="")
)
def to_tuples(self, na_tuple=True):
tuples = com.asarray_tuplesafe(zip(self.left, self.right))
if not na_tuple:
# GH 18756
tuples = np.where(~self.isna(), tuples, np.nan)
return tuples
@Appender(_extension_array_shared_docs["repeat"] % _shared_docs_kwargs)
def repeat(self, repeats, axis=None):
nv.validate_repeat(tuple(), dict(axis=axis))
left_repeat = self.left.repeat(repeats)
right_repeat = self.right.repeat(repeats)
return self._shallow_copy(left=left_repeat, right=right_repeat)
_interval_shared_docs["contains"] = textwrap.dedent(
"""
Check elementwise if the Intervals contain the value.
Return a boolean mask whether the value is contained in the Intervals
of the %(klass)s.
.. versionadded:: 0.25.0
Parameters
----------
other : scalar
The value to check whether it is contained in the Intervals.
Returns
-------
boolean array
See Also
--------
Interval.contains : Check whether Interval object contains value.
%(klass)s.overlaps : Check if an Interval overlaps the values in the
%(klass)s.
Examples
--------
%(examples)s
>>> intervals.contains(0.5)
array([ True, False, False])
"""
)
@Appender(
_interval_shared_docs["contains"]
% dict(
klass="IntervalArray",
examples=textwrap.dedent(
"""\
>>> intervals = pd.arrays.IntervalArray.from_tuples([(0, 1), (1, 3), (2, 4)])
>>> intervals
<IntervalArray>
[(0, 1], (1, 3], (2, 4]]
Length: 3, closed: right, dtype: interval[int64]
"""
),
)
)
def contains(self, other):
if isinstance(other, Interval):
raise NotImplementedError("contains not implemented for two intervals")
return (self.left < other if self.open_left else self.left <= other) & (
other < self.right if self.open_right else other <= self.right
)
_interval_shared_docs["overlaps"] = textwrap.dedent(
"""
Check elementwise if an Interval overlaps the values in the %(klass)s.
Two intervals overlap if they share a common point, including closed
endpoints. Intervals that only have an open endpoint in common do not
overlap.
.. versionadded:: 0.24.0
Parameters
----------
other : %(klass)s
Interval to check against for an overlap.
Returns
-------
ndarray
Boolean array positionally indicating where an overlap occurs.
See Also
--------
Interval.overlaps : Check whether two Interval objects overlap.
Examples
--------
%(examples)s
>>> intervals.overlaps(pd.Interval(0.5, 1.5))
array([ True, True, False])
Intervals that share closed endpoints overlap:
>>> intervals.overlaps(pd.Interval(1, 3, closed='left'))
array([ True, True, True])
Intervals that only have an open endpoint in common do not overlap:
>>> intervals.overlaps(pd.Interval(1, 2, closed='right'))
array([False, True, False])
"""
)
@Appender(
_interval_shared_docs["overlaps"]
% dict(
klass="IntervalArray",
examples=textwrap.dedent(
"""\
>>> data = [(0, 1), (1, 3), (2, 4)]
>>> intervals = pd.arrays.IntervalArray.from_tuples(data)
>>> intervals
<IntervalArray>
[(0, 1], (1, 3], (2, 4]]
Length: 3, closed: right, dtype: interval[int64]
"""
),
)
)
def overlaps(self, other):
if isinstance(other, (IntervalArray, ABCIntervalIndex)):
raise NotImplementedError
elif not isinstance(other, Interval):
msg = f"`other` must be Interval-like, got {type(other).__name__}"
raise TypeError(msg)
# equality is okay if both endpoints are closed (overlap at a point)
op1 = le if (self.closed_left and other.closed_right) else lt
op2 = le if (other.closed_left and self.closed_right) else lt
# overlaps is equivalent negation of two interval being disjoint:
# disjoint = (A.left > B.right) or (B.left > A.right)
# (simplifying the negation allows this to be done in less operations)
return op1(self.left, other.right) & op2(other.left, self.right)
def maybe_convert_platform_interval(values):
"""
Try to do platform conversion, with special casing for IntervalArray.
Wrapper around maybe_convert_platform that alters the default return
dtype in certain cases to be compatible with IntervalArray. For example,
empty lists return with integer dtype instead of object dtype, which is
prohibited for IntervalArray.
Parameters
----------
values : array-like
Returns
-------
array
"""
if isinstance(values, (list, tuple)) and len(values) == 0:
# GH 19016
# empty lists/tuples get object dtype by default, but this is
# prohibited for IntervalArray, so coerce to integer instead
return np.array([], dtype=np.int64)
elif is_categorical_dtype(values):
values = np.asarray(values)
return maybe_convert_platform(values)